Agents with real tools
Models that can call your APIs, query your data and execute defined actions — with guardrails, validation and audit trails around every call.
AI engineering
AI that actually does something
Most AI work stops at a chatbot bolted onto a website. The interesting part starts where the model has to reach into real systems: agents with tools, retrieval over enterprise knowledge, and workflows where the output has consequences.
The real value of AI isn't the model. It's what you connect the model to.
Retrieval-Augmented Generation
Enterprise knowledge retrieval over documents, policies and internal data, with chunking, embeddings and ranking tuned to the domain rather than a demo.
Model Context Protocol
MCP servers that expose business systems to models in a standardised, permissioned way instead of one-off glue code per integration.
Enterprise AI architecture
Microsoft AI Foundry and Azure OpenAI deployed inside existing application landscapes, with clear boundaries between deterministic logic and model output.
Reference architecture
How a request actually flows
AI Agent
Reasoning layer that plans, selects tools and decides what context it still needs.
Want AI connected to your systems?
From retrieval over internal knowledge to agents that can safely act inside your applications.